Prediction of Survival in Surgical Unresectable Lung Cancer by Artificial Neural Networks Including Genetic Polymorphisms and Clinical Parameters

Journal of Clinical Laboratory Analysis

Volumn 17, Issue 6, 2003, Pages 229-234

  Hsia, Te Chun ^a   Chiang, Hung Chih ^b,c   Chiang, David ^b   Hang, Liang Wen ^a   Tsai, Fuu Jen ^a   Chen, Wen Chi ^a  

^a CHINA MEDICAL UNIVERSITY HOSPITAL   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^c CHING YUN UNIVERSITY   (Taiwan)

Author keywords

Artificial neural networks; Data mining; Lung cancer; p21 gene; p53 gene; Survival

Indexed keywords

PROTEIN P21; PROTEIN P53;

ACCURACY; ADULT; ADVANCED CANCER; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER STAGING; CANCER SURVIVAL; COMPUTER; DATA MINING; FEMALE; GENE; GENETIC MARKER; GENETIC POLYMORPHISM; HUMAN; INFORMATION PROCESSING; LUNG CANCER; MAJOR CLINICAL STUDY; MALE; MODEL; P21 GENE; PATHOLOGY; PREDICTION; SURVIVAL TIME; TUMOR SUPPRESSOR GENE;

ADULT; AGED; AGED, 80 AND OVER; CARCINOMA, NON-SMALL-CELL LUNG; CYCLIN-DEPENDENT KINASE INHIBITOR P21; CYCLINS; FEMALE; FORECASTING; GENES, P53; HUMANS; LUNG NEOPLASMS; MALE; MIDDLE AGED; NEOPLASM STAGING; NEURAL NETWORKS (COMPUTER); POLYMORPHISM, SINGLE NUCLEOTIDE; SURVIVAL RATE;

EID: 0347589503     PISSN: 08878013     EISSN: None     Source Type: Journal    
DOI: 10.1002/jcla.10102     Document Type: Article

References (22)

1. Department of Health, the Executive Yuan. Republic of China, general health statistics, 1997. In: Health and vital statistics. Taipei, Taiwan: R.O.C. Press; 1998. p 86-108.
2. Finne P, Finne R, Stenman UH. Neural network analysis of clinicopathological factors in urological disease: a critical evaluation of available techniques. BJU Int 2001;88:825-831.
3. Lin JS, Ligomenides PA, Freedman MT, et al. Application of artificial neural networks for reduction of false-positive detections in digital chest radiographs. Proc Annu Symp Comput Appl Med Care 1993;434-438.
4. Wu YC, Doi K, Giger ML, et al. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme. J Digit Imaging 1994;7:196-207.
5. Biganzoli E, Boracchi P, Mariani L, et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med 1998;17:1169-1186.
6. Loder N. Genetic variations can point the way to disease gene. Nature 1999;401:734.
7. Shih CM, Lin PT, Wang HC, et al. Lack of evidence of association of p21 WAF1/CIP1 polymorphism with lung cancer susceptibility and prognosis in Taiwan. JPN J Cancer Res 2000;91:9-15.
8. Wang YC, Chen CY, Chen SK, et al. p53 codon 72 polymorphism in Taiwanese lung cancer patients: association with lung cancer susceptibility and prognosis. Clin Cancer Res 1999;5:129-134.
9. Wang YC, Lee HS, Chen SK, et al. Prognostic significance of p53 codon 72 polymorphism in lung carcinomas. Eur J Cancer 1999;35:226-230.
10. Anonymous. The world health organization. Histological typing of lung tumors. Neoplasia 1982;29:111-123.
11. Moutain CF. Revisions in the international system for staging lung cancer. Chest 1997;111:1710-1717.
12. Storey A, Thomas M, Kalita A, et al. Role of a p53 polymorphism in the development of human papillomavirus-associated cancer. Nature 1998;393:229-234.
13. Li YJ, Laurent-Puig P, Salmon RJ, et al. Polymorphisms and probable lack of mutation in the WAF1-CIP1 gene in colorectal cancer. Oncogene 1995;10:599-601.
14. Abratt RP, Sandler A, Crino L. Combined cisplatin and gemcitabine for NSCLC: influence of scheduling on toxicity and drug delivery. Semin Oncol 1998;25(suppl):35-43.
15. Treat J. Part II: treatment of non-small cell lung cancer: chemotherapy. In: Fishman AP, Elias JA, Fishman JA, et al., editors. Fishman's pulmonary diseases and disorders, Vol. II, 3rd ed. New York: McGraw-Hill 1998; p 1797-1818.
16. SPSS Data Mining. 1998. http://www.spss.com/datamine/whitpap.htm.
17. Binder M, Steiner A, Schwarz M, et al. Application of an artificial neural network in epiluminescence microscopy pattern analysis of pigmented skin lesions: a pilot study. Br J Dermatol 1994;130: 460-465.
18. de Tommaso M, Sciruicchio V, Bellotti R, et al. Photic driving response in primary headache: diagnostic value tested by discriminant analysis and artificial neural network 'classifiers. Ital J Neurol Sci 1999;20:23-28.
19. Mobley BA, Schechter E, Moore WE, et al. Predictions of coronary artery stenosis by artificial neural network. Artif Intell Med 2000;18:187-203.
20. Cho SJ, Hermsmeier MA. Genetic algorithm guided selection: variable selection and subset selection. J Chem Inf Comput Sci 2002;42:927-936.
21. Matsuki Y, Nakamura K, Watanabe H, et al. Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high resolution CT: evaluation with receiver operating characteristics analysis. AJR Am J Roentgenol 2002;178:657-663.
22. Zhou ZH, Jiang Y, Yang YB, et al. Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 2002;24:25-36.